{"id":"W4408650686","doi":"10.1038/s41467-025-57995-0","title":"Computational memory capacity predicts aging and cognitive decline","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Center for Innovative Medicine; HORIZON EUROPE Framework Programme; UK Dementia Research Institute; Vetenskapsrådet; Hjärnfonden; University College London; National Institute for Health and Care Research; Karolinska Institutet; EU Joint Programme – Neurodegenerative Disease Research; Familjen Erling-Perssons Stiftelse; Stiftelsen för Gamla Tjänarinnor; McGill University; European Commission; Alzheimer's Drug Discovery Foundation; Alzheimer's Association","keywords":"Cognition; Working memory; Neuroscience; Healthy aging; Effects of sleep deprivation on cognitive performance; Cohort; Computer science; Aging brain; Cognitive aging; Psychology; Medicine; Gerontology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005294896,0.0003567575,0.0002375702,0.0006417784,0.0001844552,0.0005720603,0.0002643815,0.0003784681,0.001183827],"category_scores_gemma":[0.003609125,0.000124321,0.0001824833,0.0002897456,0.0003535929,0.0006216401,0.0005123395,0.0004392297,0.0002374147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001627999,"about_ca_system_score_gemma":0.0001232525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001842787,"about_ca_topic_score_gemma":0.002015522,"domain_scores_codex":[0.9999294,0.0000113232,0.000008489247,0.00003093073,0.00001086265,0.000009028762],"domain_scores_gemma":[0.9989032,0.0002974362,0.0004018347,0.0001580145,0.000135828,0.0001036543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005067933,0.0001417923,0.9685392,0.00004962552,0.0002156776,0.0001368204,0.0002734592,0.002024164,0.005534288,0.0004452151,0.0004832317,0.02164982],"study_design_scores_gemma":[0.000006175092,0.0001940244,0.9904517,0.00001318549,0.00006081115,0.0003102711,0.00009342878,0.005211203,0.001294744,0.002071347,0.0002807158,0.00001235227],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982849,0.0002222169,0.0007186789,0.0000427969,0.000004453283,0.000005503966,0.000327114,0.00001578055,0.0003785563],"genre_scores_gemma":[0.9991758,0.00008130852,0.0002980599,0.00001174734,0.000006618741,0.000005259893,0.0002205499,0.000003191729,0.0001973862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001842787,"threshold_uncertainty_score":0.003960311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03154423386221594,"score_gpt":0.3133651659302784,"score_spread":0.2818209320680624,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}